Design of robust nonsquare constrained model‐predictive control

A model‐predictive control (MPC) design methodology for processes with more manipulated inputs than outputs is developed. Essential features of the proposed approach are the following: the on‐line optimization minimizes an objective function based on the l 2 norm; an end‐condition equation is utilized; model uncertainty is considered as upper and lower bounds on the pulse‐response‐model coefficients; hard constraints on the input and move‐size variables and soft constraints on the output variables are posed. A major difference between square and nonsquare MPC is that in the former the end‐condition can be used directly, while in the latter a nonlinear programming problem needs to be solved during the design phase to select values for the input move suppression coefficients. This technique is illustrated through a number of simulations and application to a real industrial process.